class_docstr: Iterable dataset that returns constant length chunks of tokens from stream of text files.
      Args:
          tokenizer (Tokenizer): The processor used for proccessing the data.
          dataset (dataset.Dataset): Dataset with text files.
          infinite (bool): If True the iterator is reset after dataset reaches end else stops.
          seq_length (int): Length of token sequences to return.
          num_of_sequences (int): Number of token sequences to keep in buffer.
          chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
          tokenized (bool): If true we use a pretokenized dataset.
code: def __init__(
      self,
      tokenizer,
      dataset,
      infinite=False,
      seq_length=1024,
      num_of_sequences=1024,
      chars_per_token=3.6,
      tokenized=False,
  ):
      self.tokenizer = tokenizer
      self.concat_token_id = tokenizer.bos_token_id
      self.dataset = dataset
      self.seq_length = seq_length
      self.epoch = 0
      self.infinite = infinite
      self.current_size = 0
      self.tokenized = tokenized
  
      if self.tokenized:
          self.max_buffer_size = seq_length * num_of_sequences
          self.content_field = "input_ids"
      else:
          self.max_buffer_size = (
              seq_length * chars_per_token * num_of_sequences
          )
          self.content_field = "content"
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 1 to create a new ConstantLengthDataset object from inherited base classes: IterableDataset with example: obj = ConstantLengthDataset(tokenizer, dataset, infinite, seq_length, num_of_sequences, chars_per_token, tokenized)